Bibliographic record
Abstract
I don't know how to answer this question of different kinds of minds with different kinds of interests.What hooks them on?How do you direct them to be interested?One way is by a kind of forceyou have to take this course, you have to take that examination!It's a very effective way.Many people go through schools that way, but maybe there's a more effective way.I'm sorry but after many, many years of trying to teach, and trying all different kinds of methods, I really don't know how to do it.I got a kick when I was a boy from my father telling me things.[Richard P. Feynman, BBC Interview, 1981] As a plant biochemist, I was often as vocationally distant from colleagues in the Faculties of Medicine in which I spent my entire professional life, and from fellow applicants to the Medical Research Council of Canada, the sole source of my research funding, as I would have been if I had remained in my original specialty: history and classical languages.A plant bias likely began seeping into my being when my mother, the spirited daughter of a prospering suburban Toronto market gardener, decided to give birth at her parents' Berry Road home, which appropriately neighboured the property of Mr. Seed, owner of what was widely acknowledged to be the most beautiful market garden in the township.My father, whose Oakwood Cycle & Radio shop in Toronto served us well in good times and bad, had a passion for angling, and with wife and children in tow, he fished the trout streams that criss-crossed the working farms north of Toronto.By the time I was 15 years old, my parents were able to purchase a 100-acre plot of thick woods, rolling hills and streams in the Albion hills north of Toronto, directly neighbouring the now-celebrated property of film director Norman Jewison.This early and long exposure to trim market gardens and lush landscape had an indelible impact.There is no doubt my fondness for plants, the plant world, and experimentation with plants, cultivated and natural, had its origins in the influences I have just described.This facile 'osmotic' ab-
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.027 |
| Insufficient payload (model declined to judge) | 0.035 | 0.033 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".